Data Tech

Schools use targeted interventions to help students at risk of school dropout or late graduation. Identifying these students takes significant investment in people, process, and technology, and warning signs are often context-specific and scattered across data sources. Extremely high counselor caseloads compound the problem, and are worse in schools serving children with other structural disadvantages. We describe how machine learning technology can improve existing educational systems like the Minnesota Early Indicator and Response System, and provide details on this new technology's statewide implementation in Kentucky. We discuss key technical challenges for early warning data systems and best practices for overcoming them. These challenges specifically include foregrounding fairness, accountability, and transparency, and we offer a discussion of how computational public policy systems can mitigate or worsen equitable access for all students.